Age Differences in Cognitive and Affective Theory of Mind: Concurrent Contributions of Neurocognitive Performance, Sex, and Pulse Pressure
Bibliographic record
Abstract
OBJECTIVES: Theory of mind (ToM) allows us to detect and make inferences about cognitive and affective mental states. Mixed findings exist regarding (a) age differences in cognitive and affective ToM and (b) what mechanisms may underlie changes in the two components. We addressed these questions by examining the unique and joint contributions of neurocognitive performance, pulse pressure (PP), and biological sex to age differences in cognitive and affective ToM. METHOD: We tested 86 young and 85 older adults on standardized measures of neurocognitive performance and ToM. Predictors were derived from demographics (sex), in-office PP, and measures of executive functions, semantic memory, and episodic memory. We used path analysis to identify concurrent predictors of cognitive and affective ToM between groups and invariance analyses to assess age differences in the relative strength of identified predictors. RESULTS: We demonstrated robust age differences in cognitive and affective ToM. Certain neurocognitive predictors of ToM were more salient among older individuals; most predictors were shared across age groups and equivalent in magnitude. DISCUSSION: To our knowledge, this study represents the most comprehensive investigation to date of predictors of ToM in aging. Findings highlight the need for continued investigation of ToM within a multidimensional framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".